In numerous domains where image analysis plays a role, for example in medical imaging, it is important to be able to determine which images are blurred and to what extent. The general problem is determining which images are usable, for example for diagnostic purposes.
In the past, the evaluation was done using actual images viewed by a specialist or practitioner. Today, images are or can be digitized, meaning defined as discrete pixels or elemental points of the image. Each pixel is associated with one or more numerical parameters which give the pixel its colorimetric value according to a predetermined coding system. A known example of such coding systems is HSB (hue, saturation, brightness), or its variant HSV (hue, saturation, value).
Various techniques already exist for quantifying the blur of a digital image. Issues then arise concerning computation time, as this must be shorter when processing many images, and the relevance of the result.
Methods exist for testing the sharpness of a simple image. Some methods are fast computationally (about 100 ms per 512×512 pixel image), but not very reliable (camera auto-focus, color intensity analysis). These methods allow comparing a certain number of images fairly efficiently and then choosing the sharpest one, although without being able to measure the sharpness in absolute terms.
Other methods (Fourier transform, wavelet transform) are more complex and slower computationally (about 500 ms to 3s per 512×512 pixel image) and have other disadvantages (impossible to differentiate a uniform image from a blurred image).
Many articles exist concerning blur detection in digital images. One notable example is Blur Detection for Digital Images Using Wavelet Transform by HANGHANG TONG, MINGJING LI, HONGJIANG ZHANG, CHANGSHUI ZHANG; 2004 IEEE International Conference on multimedia and expo: ICME 27-30/06/2004, Taipei, Taiwan. This article presents a blur detection algorithm using wavelet transform. The analysis is also a function of the square of the number of pixels. Thus the computation time for an image is about one second. In addition, the blurred images used for the tests were produced using sharp source images to which various types of digital blurring were applied (blur matrices). Digital blur is much easier to detect reliably than “analog” blur due for example to improper focus or unanticipated movement during the image capture.
The aim of the present invention is to overcome some or all of the above disadvantages, in particular to provide a method for quantifying blur that is both very fast and at least as reliable, if not more reliable, than existing methods, in particular for “analog” type blur.